Department of Computer Science and Information Systems
Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1928
Browse
3 results
Search Results
Item AENeT: an attention-enabled neural architecture for fake news detection using contextual features(Springer, 2021) Narang, Pratik; Sharma, YashvardhanIn the current era of social media, the popularity of smartphones and social media platforms has increased exponentially. Through these electronic media, fake news has been rising rapidly with the advent of new sources of information, which are highly unreliable. Checking off a particular news article is genuine or fake is not easy for any end user. Search engines like Google are also not capable of telling about the fakeness of any news article due to its restriction with limited query keywords. In this paper, our end goal is to design an efficient deep learning model to detect the degree of fakeness in a news statement. We propose a simple network architecture that combines the use of contextual embedding as word embedding and uses attention mechanisms with relevant metadata available. The efficacy and efficiency of our models are demonstrated on several real-world datasets. Our model achieved 46.36% accuracy on the LIAR dataset, which outperforms the current state of the art by 1.49%.Item Political Opinion Mining from Twitter(IJISSS, 2016) Sharma, YashvardhanTwitter is one of the most popular micro-blogging platform for people to express their political views in and around the elections. Hence during pre-elections twitter becomes a rich resource of data to understand the changing tenor of political leaders with time. During this time, when views, opinions and judgments are shared so prolifically through online media, tools which can provide the crux of this content are paramount. In this paper the authors have developed one such sentiment analysis tool to analyze the changing political views of persons with time. Using the tool they classify the tweets as positive, negative or neutral and studying it over time the authors successfully estimate the mood of the person. The authors have also developed a specialized phonetic dictionary to provide better approximation for most commonly used slangs and abbreviations.Item TwiBiNG: A Bipartite News Generator Using Twitter(CEUR, 2014) Sharma, YashvardhanOnline Journalism is being seen as future of Journalism. News Professionals are vying to capture newsworthy stories that emerge from crowd. Live Social Media especially Twitter is generating enormous volumes of data every minute. It becomes difficult to select credible and relevant tweets that may form quality news among others. The problem intensifies due to the freedom of Twitter being an informal language. Generating headlines by solving this problem may still not be relevant and may face the question of authenticity. Given a set of keywords and a time period this problem becomes manageable and can be solved efficiently. We propose a bipartite algorithm that clusters authentic tweets based on key phrases and ranks the clusters based on trends in each timeslot.